The assumption of Gaussian noise in the system and measurement model has been standard practice for target tracking algorithm development
for many years. For problems involving manoeuvring targets this is known to be an over-simplification and a potentially poor approximation. In this paper the use of heavy-tailed distributions is suggested as a means of efficiently modelling the behaviour of manoeuvring targets with a single dynamic model. We exploit the fact
that all heavy-tailed distributions can be written as scale mixture of Normals to propose a Rao-Blackwellised particle filter (SMNPF) where particles sample the history of the continuous scale parameter and a Kalman filter is used to conduct the associated filtering for each particle. Schemes are proposed for making the proposal of new particles efficient. Performance of a heavy-tailed system model implemented via the SMNPF filter is compared against an IMM for a sample trajectory taken from a benchmark problem.
We propose a very generic Bayesian framework for the principled exploitation of probabilistic batch-learning technologies for real-time state estimation. To illustrate our concepts, we derive a nonlinear filtering/smoothing solution for a challenging case study in target tracking. We also demonstrate the application of Markov chain Monte Carlo (MCMC) sampling methods as a computational tool within our framework. Finally, we present simulation results, benchmarked against a comparable particle filter.
The problem of maintaining track on a primary target in the presence spurious objects is addressed. Recursive and batch filtering approaches are developed. For the recursive approach, a Bayesian track splitting filter is derived which spawns candidate tracks if there is a possibility of measurement misassociation. The filter evaluates the probability of each candidate track being associated with the primary target. The batch filter is a Markov-chain Monte Carlo (MCMC) algorithm which fits the observed data sequence to models of target dynamics and measurement-track association. Simulation results are presented.
A Bayesian technique is applied to the target acquisition problem at handover from the fire control to the missile seeker. This is a multiple hypothesis scheme which includes an explicit model of the possible misalignment or bias between the fire control and seeker coordinate frames. This method is compared with a linear least cost assignment technique which may be implemented via the Munkres fast search algorithm.